基于粒子群算法的悬架PID控制器参数整定研究

Research on parameter tuning of suspension PID controller based on PSO algorithm

工程实际中确定PID控制器参数主要通过理论计算或试凑法,适用场景单一且具有局限性,整定耗时长、控制效果不理想。为此提出使用粒子群算法来优化PID控制器参数,基于粒子间的数据传递特性,判断自身最佳位置和速度与群体最佳位置和速度之间的联系,从而找到更接近全局最优的PID参数配置。基于MATLAB/Simulink搭建二自由度悬架模型,设计PID控制器,仿真比较改进前后的悬架性能指标。结果表明,优化后悬架系统的动力学响应特性均优于工程整定法确定的PID控制系统,车身垂向加速度降低了24.3%,提高了悬架的舒适性。

In practical engineering,the determination of PID controller parameters was mainly through theoretical calculations or trial and error methods,which were applicable to a single scenario and have limitations.The tuning time was long and the control effect was not ideal.Therefore,it was proposed to use particle swarm optimization algorithm to optimize the PID controller parameters.Based on the data transfer characteristics between particles,the relationship between their own optimal position and velocity and the group′s optimal position and velocity was determined,in order to find a PID parameter configuration that was closer to the global optimum.Building a two degree of freedom suspension model based on MATLAB∕Simulink,designing a PID controller,and simulating and comparing the suspension performance indicators before and after improvement.The results showed that the optimized suspension system had better dynamic response characteristics than the PID control system determined by engineering tuning method,with a 24.3%reduction in vertical acceleration of the vehicle body and an improvement in suspension comfort.